An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling

Jetka, Tomasz; Nienałtowski, Karol; Filippi, Sarah; Stumpf, Michael; Komorowski, Michał

doi:10.1038/s41467-018-07085-1

Cited by 33 publications

(41 citation statements)

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“…We expect that similar, future applications of the FSP-based Fisher information to be valuable in other systems and synthetic biology contexts where scientists seek to explore how different cellular properties affect the transmission of information between cells or from cells to human observers. Indeed, similar ideas have been explored recently using classical information theory in [35–37], and recent work in [38] has noted the close relationship between Fisher information and the channel capacity of biochemical signaling networks.…”

Section: Discussionmentioning

confidence: 98%

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Fox

Neuert

Munsky

2019

Preprint

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24Modern biological experiments are becoming increasingly complex, and designing these experi-25 ments to yield the greatest possible quantitative insight is an open challenge. Increasingly, compu-26 tational models of complex stochastic biological systems are being used to understand and predict 27 biological behaviors or to infer biological parameters. Such quantitative analyses can also help 28 to improve experiment designs for particular goals, such as to learn more about specific model 29 mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment 30 design is to use the Fisher information matrix (FIM), which quantifies the expected information a 31 particular experiment will reveal about model parameters. The Finite State Projection based FIM 32 (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory 33 systems, whose complex response distributions do not satisfy standard assumptions of Gaussian 34 variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response 35 genes in S. cerevisae under time-varying MAPK induction. We validate this FSP-FIM analysis 36 and use it to optimize the number of cells that should be quantified at particular times to learn as 37 much as possible about the model parameters. We then demonstrate how the FSP-FIM approach 38 can be extended to explore how different measurement times or genetic modifications can help to 39 minimize uncertainty in the sensing of extracellular environments, such as external salinity mod-40 ulations. This work demonstrates the potential of quantitative models to not only make sense of 41 modern biological data sets, but to close the loop between quantitative modeling and experimental 42 data collection. 44The standard approach to design experiments has been to rely entirely on expert knowl-45 edge and intuition. However, as experimental investigations become more complex and 46 seek to examine systems with more subtle non-linear interactions, it becomes much harder 47 to improve experimental designs using intuition alone. This issue has become especially 48 relevant in modern single-cell-single-molecule investigations of gene regulatory processes. 49Performing such powerful, yet complicated experiments involves the selection from among 50 a large number of possible experimental designs, and it is often not clear which designs 51 will provide the most relevant information. A systematic approach to solve this problem is 52 model-driven experiment design, in which one uses an assumed (and potentially incorrect) 53 mathematical model of the system to estimate and optimize the value of potential exper-54 imental settings. In recent years, model-driven experiment design has gained traction for 55 biological models of gene expression, whether in the Bayesian setting [1] or using Fisher 56 information for deterministic models [2], and even in the stochastic, single-cell setting [3-57 6]. Despite the promise and active development of model-driven e...

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Section: Discussionmentioning

confidence: 98%

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Fox

Neuert

Munsky

2019

Preprint

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“…To address the possibility of having too little data, we fit the model to six step ( # ) data simultaneously ( Figure 3A, red), and we predicted the signaling dynamics upon all remaining kinetic stimulations (48 data sets). We then compared model predictions of signaling dynamics Figure 4B), we developed a Fisher Information Matrix (FIM) analysis framework to directly estimate the uncertainties of model parameters under different experiment designs (Apgar et al, 2010;Fox and Munsky, 2019;Hagen et al, 2013;Jetka et al, 2018;Komorowski et al, 2011) (STAR Methods). For example, each row in Figure S3 represents such an experiment design.…”

Section: Lack Of Kinetic Stimulation Diversity Limits Model Predictiomentioning

confidence: 99%

“…The Fisher information matrix (FIM) analysis was used to estimate expected parameter uncertainty for different experiment designs (Apgar et al, 2010;Fox and Munsky, 2019;Hagen et al, 2013;Jetka et al, 2018;Komorowski et al, 2011). The FIM provides the amount of information an observable could provide around an unknown parameter, and it has been extensively used to estimate how well potential experiments will constrain model parameters (Apgar et al, 2008;Bandara et al, 2009;Fox and Munsky, 2019;Sinkoe et al, 2017;Stewart-Ornstein et al, 2017).…”

Section: Optimization Algorithmmentioning

confidence: 99%

Diverse cell stimulation kinetics identify predictive signal transduction models

Jashnsaz

Fox

Hughes

et al. 2020

Preprint

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The drive to understand cell signaling responses to environmental, chemical and genetic perturbations has produced outstanding fits of computational models to increasingly intricate experiments, yet predicting quantitative responses for new biological conditions remains challenging. Overcoming this challenge depends not only on good models and detailed experimental data but perhaps more so on how well the two are integrated. Our quantitative, live single-cell fluorescence imaging datasets and computational framework to model generic signaling networks show how different changing environments (hereafter 'kinetic stimulations') probe and result in distinct pathway activation dynamics. Utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional step-change stimulations. To demonstrate our approach's generality, we use identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.

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“…Therefore, we use the approximation given by the asymptotic capacity. The asymptotic capacity calculates the information capacity exactly for a large number of statistically independent copies of a given signaling system and then regresses back the capacity of a single copy [51].…”

Section: Channel Capacity For Unregulated Gene Expressionmentioning

confidence: 99%

“…and the general formulation for asymptotic channel capacity can be computed from the Fisher information using [51]. For the gamma distribution derived in (3), the Fisher information matrix is given by…”

Section: Channel Capacity For Unregulated Gene Expressionmentioning

confidence: 99%

Channel Capacity Computations for Unregulated and Autoregulated Gene Expression

Vahdat

Nienałtowski

Farooq

et al. 2019

Preprint

Self Cite

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How living cells can reliably process biochemical cues in the presence of molecular noise is not fully understood.Here we investigate the fidelity of information transfer in the expression of a single gene. We use the established model of gene expression to examine how precisely the protein levels can be controlled by two distinct mechanisms: (i) the transcription rate of the gene, or (ii) the translation rate for the corresponding mRNA. The fidelity of gene expression is quantified with the information-theoretic notion of information capacity. Derived information capacity formulae reveal that transcriptional control generally provides a tangibly higher capacity as compared to the translational control. We next introduce negative feedback regulation in gene expression, where the protein directly inhibits its own transcription. While negative feedback reduces noise in the level of the protein for a given input signal, it also decreases the input-to-output sensitivity. Our results show that the combined effect of these two opposing forces is a reduced capacity in the presence of feedback. In summary, our analysis presents the first analytical quantification of information transfer in simple gene expression models, which provides insight into the fidelity of basic gene expression control mechanisms.

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An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling

Cited by 33 publications

References 50 publications

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Diverse cell stimulation kinetics identify predictive signal transduction models

Channel Capacity Computations for Unregulated and Autoregulated Gene Expression

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